Multi-station water level forecasting using advanced graph convolutional networks with adversarial learning

This paper presents an advanced graph convolutional network model, enhanced with Wasserstein distance-based adversarial learning (WD-ACGN), addressing the limitations of existing single-station and less explored multi-station water level forecasting approaches. The model features a novel coupled module for effectively capturing short and long-term dependencies, and a hybrid distance-based adaptive graph learning approach for spatial dependencies. This spatial dependency analysis enables rapid deployment in different coastal settings. Adversarial learning with gradient penalty further refines the model's performance. Our model, applied to datasets from China's Zhejiang coast and Daya Bay, outperforms baselines with a notable 12-h average root mean square error of 6.77 cm at 16 Zhejiang stations, proving its efficacy in varied maritime environments. Ablation studies validate the contribution of each model component, highlighting their collective impact on overall efficacy. Notably, the model showcases robustness in tropical cyclone scenarios and reliable results when tested with real-world observational data, underlining its potential for versatile applications in ocean engineering.